{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验数据\n",
    "这里使用中文科幻小说《三体》为例子，含注释共213章，使用正则表达式构建三体小说数据集，该数据集涵\n",
    "- chapterid 第几章\n",
    "- title 章(节)标题\n",
    "- text 每章节的文本内容(分词后以空格间隔的文本，形态类似英文)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /var/folders/sc/3mnt5tgs419_hk7s16gq61p80000gn/T/jieba.cache\n",
      "Loading model cost 0.592 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chapterid</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>科学边界(1)</td>\n",
       "      <td>科学 边界 ( 1 ) \\n \\n         恋上你 看书 网   630book...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>科学边界(2)</td>\n",
       "      <td>科学 边界 ( 2 ) \\n \\n         恋上你 看书 网   630book...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>台球</td>\n",
       "      <td>台球 \\n \\n         恋上你 看书 网   630bookla   ， 最快...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>211</td>\n",
       "      <td>【时间之外，我们的宇宙】(2)</td>\n",
       "      <td>【 时间 之外 ， 我们 的 宇宙 】 ( 2 ) \\n \\n         恋上你 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>212</td>\n",
       "      <td>【时间之外，我们的宇宙】(3)</td>\n",
       "      <td>【 时间 之外 ， 我们 的 宇宙 】 ( 3 ) \\n \\n         恋上你 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>213</td>\n",
       "      <td>注释</td>\n",
       "      <td>注释 \\n \\n         恋上你 看书 网   630bookla   ， 最快...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>213 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chapterid            title  \\\n",
       "0            1          科学边界(1)   \n",
       "1            2          科学边界(2)   \n",
       "2            3               台球   \n",
       "..         ...              ...   \n",
       "210        211  【时间之外，我们的宇宙】(2)   \n",
       "211        212  【时间之外，我们的宇宙】(3)   \n",
       "212        213               注释   \n",
       "\n",
       "                                                  text  \n",
       "0      科学 边界 ( 1 ) \\n \\n         恋上你 看书 网   630book...  \n",
       "1      科学 边界 ( 2 ) \\n \\n         恋上你 看书 网   630book...  \n",
       "2      台球 \\n \\n         恋上你 看书 网   630bookla   ， 最快...  \n",
       "..                                                 ...  \n",
       "210    【 时间 之外 ， 我们 的 宇宙 】 ( 2 ) \\n \\n         恋上你 ...  \n",
       "211    【 时间 之外 ， 我们 的 宇宙 】 ( 3 ) \\n \\n         恋上你 ...  \n",
       "212    注释 \\n \\n         恋上你 看书 网   630bookla   ， 最快...  \n",
       "\n",
       "[213 rows x 3 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import jieba\n",
    "import re\n",
    "pd.set_option('display.max_rows', 6)\n",
    "\n",
    "raw_texts = open('三体.txt', encoding='utf-8').read()\n",
    "texts = re.split('第\\d+章', raw_texts)\n",
    "texts = [text for text in texts if text]\n",
    "#中文多了下面一行代码（构造用空格间隔的字符串）\n",
    "texts = [' '.join(jieba.lcut(text)) for text in texts if text]\n",
    "titles = re.findall('第\\d+章 (.*?)\\n', raw_texts)\n",
    "\n",
    "data = {'chapterid': list(range(1, len(titles)+1)),\n",
    "        'title': titles,\n",
    "        'text': texts}\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tidytextpy库\n",
    "- get_stopwords 停用词表\n",
    "- get_sentiments 情感词典\n",
    "- unnest_tokens 分词函数\n",
    "- bind_tf_idf  计算tf-idf\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 停用词表\n",
    "get_stopwords(language)  获取对应语言的停用词表，目前仅支持chinese和english两种语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['、',\n",
       " '。',\n",
       " '〈',\n",
       " '〉',\n",
       " '《',\n",
       " '》',\n",
       " '一',\n",
       " '一些',\n",
       " '一何',\n",
       " '一切',\n",
       " '一则',\n",
       " '一方面',\n",
       " '一旦',\n",
       " '一来',\n",
       " '一样',\n",
       " '一般',\n",
       " '一转眼',\n",
       " '七',\n",
       " '万一',\n",
       " '三']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tidytextpy import get_stopwords\n",
    "\n",
    "cn_stps = get_stopwords('chinese')\n",
    "#前20个中文的停用词\n",
    "cn_stps[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " 'her',\n",
       " 'hers']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_stps = get_stopwords()\n",
    "#前20个英文文的停用词\n",
    "en_stps[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 情感词典\n",
    "**get_sentiments('词典名')** 调用词典，返回词典的dataframe数据。\n",
    "\n",
    "- [afinn](http://www2.imm.dtu.dk/pubdb/pubs/6010-full.html) sentiment取值-5到5\n",
    "- [bing](https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html) sentiment取值为positive或negative\n",
    "- [nrc](http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm) sentiment取值为positive或negative，及细粒度的情绪分类信息\n",
    "- [dutir](https://github.com/ZaneMuir/DLUT-Emotionontology) sentiment为中文七种情绪类别（细粒度情绪分类信息）\n",
    "- hownet sentiment为positive或negative\n",
    "\n",
    "其中hownet和dutir为**中文情感词典**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>惊</td>\n",
       "      <td>冷不防</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>惊</td>\n",
       "      <td>惊动</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>惊</td>\n",
       "      <td>珍闻</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27411</th>\n",
       "      <td>惧</td>\n",
       "      <td>匆猝</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27412</th>\n",
       "      <td>惧</td>\n",
       "      <td>忧心仲忡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27413</th>\n",
       "      <td>惧</td>\n",
       "      <td>面面厮觑</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27414 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sentiment  word\n",
       "0             惊   冷不防\n",
       "1             惊    惊动\n",
       "2             惊    珍闻\n",
       "...         ...   ...\n",
       "27411         惧    匆猝\n",
       "27412         惧  忧心仲忡\n",
       "27413         惧  面面厮觑\n",
       "\n",
       "[27414 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tidytextpy import get_sentiments\n",
    "\n",
    "#大连理工大学情感本体库，共七种情绪（sentiment）\n",
    "get_sentiments('dutir')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abacus</td>\n",
       "      <td>trust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abandon</td>\n",
       "      <td>fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abandon</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13898</th>\n",
       "      <td>zest</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13899</th>\n",
       "      <td>zest</td>\n",
       "      <td>trust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13900</th>\n",
       "      <td>zip</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13901 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          word sentiment\n",
       "0       abacus     trust\n",
       "1      abandon      fear\n",
       "2      abandon  negative\n",
       "...        ...       ...\n",
       "13898     zest  positive\n",
       "13899     zest     trust\n",
       "13900      zip  negative\n",
       "\n",
       "[13901 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_sentiments('nrc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分词\n",
    "``unnest_tokens(__data, output, input)``\n",
    "- ``__data`` 待处理的dataframe数据\n",
    "- output 新生成的dataframe中，用于存储分词结果的字段名\n",
    "- input 待分词数据的字段名(待处理的dataframe数据)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chapterid</th>\n",
       "      <th>title</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>科学边界(1)</td>\n",
       "      <td>科学</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>科学边界(1)</td>\n",
       "      <td>边界</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>科学边界(1)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>213</td>\n",
       "      <td>注释</td>\n",
       "      <td>想到</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>213</td>\n",
       "      <td>注释</td>\n",
       "      <td>暗物质</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>213</td>\n",
       "      <td>注释</td>\n",
       "      <td>。</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>556595 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chapterid    title word\n",
       "0            1  科学边界(1)   科学\n",
       "0            1  科学边界(1)   边界\n",
       "0            1  科学边界(1)    1\n",
       "..         ...      ...  ...\n",
       "212        213       注释   想到\n",
       "212        213       注释  暗物质\n",
       "212        213       注释    。\n",
       "\n",
       "[556595 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tidytextpy import unnest_tokens\n",
    "\n",
    "tokens = unnest_tokens(df, output='word', input='text')\n",
    "tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 各章节用词量\n",
    "从这里开始会用到plydata的管道符>> 和相关的常用函数，建议大家遇到不懂的地方查阅plydata文档"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chapterid</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>211</td>\n",
       "      <td>2505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>212</td>\n",
       "      <td>2646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>213</td>\n",
       "      <td>2477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>213 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chapterid     n\n",
       "0            1  2549\n",
       "1            2  2666\n",
       "2            3  1726\n",
       "..         ...   ...\n",
       "210        211  2505\n",
       "211        212  2646\n",
       "212        213  2477\n",
       "\n",
       "[213 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from plydata import count, group_by, ungroup\n",
    "\n",
    "\n",
    "wordfreq = (df \n",
    "            >> unnest_tokens(output='word', input='text') #分词\n",
    "            >> group_by('chapterid')  #按章节分组\n",
    "            >> count() #对每章用词量进行统计\n",
    "            >> ungroup() #去除分组\n",
    "           )\n",
    "\n",
    "wordfreq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 章节用词量可视化\n",
    "使用plotnine进行可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (338899281)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from plotnine import ggplot, aes, theme, geom_line, labs, theme, element_text\n",
    "from plotnine.options import figure_size\n",
    "\n",
    "(ggplot(wordfreq, aes(x='chapterid', y='n'))+\n",
    " geom_line()+\n",
    " labs(title='三体章节用词量折线图',\n",
    "      x='章节', \n",
    "      y='用词量')+\n",
    " theme(figure_size=(12, 8),\n",
    "       title=element_text(family='Kai', size=15), \n",
    "       axis_text_x=element_text(family='Kai'),\n",
    "       axis_text_y=element_text(family='Kai'))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 情感分析\n",
    "重要的事情多重复一遍o(*￣︶￣*)o\n",
    "\n",
    "**get_sentiments('词典名')** 调用词典，返回词典的dataframe数据。\n",
    "\n",
    "- [afinn](http://www2.imm.dtu.dk/pubdb/pubs/6010-full.html) sentiment取值-5到5\n",
    "- [bing](https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html) sentiment取值为positive或negative\n",
    "- [nrc](http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm) sentiment取值为positive或negative，及细粒度的情绪分类信息\n",
    "- [dutir](https://github.com/ZaneMuir/DLUT-Emotionontology) sentiment为中文七种情绪类别（细粒度情绪分类信息）\n",
    "- hownet sentiment为positive或negative\n",
    "\n",
    "其中hownet和dutir为**中文情感词典**\n",
    "\n",
    "\n",
    "## 情感计算\n",
    "这里会用到plydata的很多知识点，大家可以查看https://plydata.readthedocs.io/en/latest/index.html 相关函数的文档。\n",
    "\n",
    "![](img/inner-join.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chapterid</th>\n",
       "      <th>negative</th>\n",
       "      <th>positive</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>-0.248322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>98.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>-0.082873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>54.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.186813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>211</td>\n",
       "      <td>56.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.131783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>212</td>\n",
       "      <td>71.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>-0.028986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>213</td>\n",
       "      <td>75.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>-0.006711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>213 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chapterid  negative  positive     score\n",
       "0            1      93.0      56.0 -0.248322\n",
       "1            2      98.0      83.0 -0.082873\n",
       "2            3      54.0      37.0 -0.186813\n",
       "..         ...       ...       ...       ...\n",
       "210        211      56.0      73.0  0.131783\n",
       "211        212      71.0      67.0 -0.028986\n",
       "212        213      75.0      74.0 -0.006711\n",
       "\n",
       "[213 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from plydata import inner_join, count, define, call\n",
    "from plydata.tidy import spread\n",
    "\n",
    "chapter_sentiment_score = (\n",
    "    df #分词\n",
    "    >> unnest_tokens(output='word', input='text') \n",
    "    >> inner_join(get_sentiments('hownet')) #让分词结果与hownet词表交集，给每个词分配sentiment\n",
    "    >> count('chapterid', 'sentiment')#统计每章中每类sentiment的个数\n",
    "    >> spread('sentiment', 'n') #将sentiment中的positive和negative转化为两列\n",
    "    >> call('.fillna', 0) #将缺失值替换为0\n",
    "    >> define(score = '(positive-negative)/(positive+negative)') #计算每一章的情感分score\n",
    ")\n",
    "\n",
    "chapter_sentiment_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三体小说情感走势\n",
    "我记得看完《三体》后，很悲观，觉得人类似乎永远逃不过宇宙的时空规律，心情十分压抑。如果对照小说进行章节的情感分析，应该整体情感分的走势大多在0以下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (364239885)>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from plotnine import ggplot, aes, geom_line, element_text, labs, theme\n",
    "\n",
    "(ggplot(chapter_sentiment_score, aes('chapterid', 'score'))+\n",
    " geom_line()+\n",
    " labs(x='章节', y='情感值score', title='《三体》小说情感走势图')+\n",
    " theme(figure_size=(12, 8),\n",
    "       title=element_text(family='Kai'))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tf-idf\n",
    "相比之前的代码，bind_tf_idf运行起来很慢很慢。所以这里用别的数据做实验"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tf-idf实验数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>docid</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>The Zen of Python, by Tim Peters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>If the implementation is hard to explain, it's...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>If the implementation is easy to explain, it m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Namespaces are one honking great idea -- let's...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    docid                                               text\n",
       "0       0                                                   \n",
       "1       1                   The Zen of Python, by Tim Peters\n",
       "2       2                                                   \n",
       "..    ...                                                ...\n",
       "19     19  If the implementation is hard to explain, it's...\n",
       "20     20  If the implementation is easy to explain, it m...\n",
       "21     21  Namespaces are one honking great idea -- let's...\n",
       "\n",
       "[22 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 6)\n",
    "\n",
    "zen = \"\"\"\n",
    "The Zen of Python, by Tim Peters\n",
    "\n",
    "Beautiful is better than ugly.\n",
    "Explicit is better than implicit.\n",
    "Simple is better than complex.\n",
    "Complex is better than complicated.\n",
    "Flat is better than nested.\n",
    "Sparse is better than dense.\n",
    "Readability counts.\n",
    "Special cases aren't special enough to break the rules.\n",
    "Although practicality beats purity.\n",
    "Errors should never pass silently.\n",
    "Unless explicitly silenced.\n",
    "In the face of ambiguity, refuse the temptation to guess.\n",
    "There should be one-- and preferably only one --obvious way to do it.\n",
    "Although that way may not be obvious at first unless you're Dutch.\n",
    "Now is better than never.\n",
    "Although never is often better than *right* now.\n",
    "If the implementation is hard to explain, it's a bad idea.\n",
    "If the implementation is easy to explain, it may be a good idea.\n",
    "Namespaces are one honking great idea -- let's do more of those!\n",
    "\"\"\"\n",
    "\n",
    "zen_split = zen.splitlines()\n",
    "\n",
    "df = pd.DataFrame({'docid': list(range(len(zen_split))),\n",
    "                  'text': zen_split})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bind_tf_idf\n",
    "tf表示词频，idf表示词语在文本中的稀缺性，两者的结合体现了一个词的信息量。找出小说中tf-idf最大的词。\n",
    "\n",
    "``bind_tf_idf(_data, term, document, n)``\n",
    "\n",
    "- ``_data`` 传入的df\n",
    "- term df中词语对应的字段名\n",
    "- document df中文档id的字段名\n",
    "- n df中词频数对应的字段名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>docid</th>\n",
       "      <th>word</th>\n",
       "      <th>n</th>\n",
       "      <th>tf</th>\n",
       "      <th>idf</th>\n",
       "      <th>tf_idf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>the</td>\n",
       "      <td>1</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.198042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>zen</td>\n",
       "      <td>1</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>0.427962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>of</td>\n",
       "      <td>1</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.897120</td>\n",
       "      <td>0.271017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>21</td>\n",
       "      <td>more</td>\n",
       "      <td>1</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>0.272339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>21</td>\n",
       "      <td>of</td>\n",
       "      <td>1</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1.897120</td>\n",
       "      <td>0.172465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>21</td>\n",
       "      <td>those</td>\n",
       "      <td>1</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>0.272339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     docid   word  n        tf       idf    tf_idf\n",
       "0        1    the  1  0.142857  1.386294  0.198042\n",
       "1        1    zen  1  0.142857  2.995732  0.427962\n",
       "2        1     of  1  0.142857  1.897120  0.271017\n",
       "..     ...    ... ..       ...       ...       ...\n",
       "137     21   more  1  0.090909  2.995732  0.272339\n",
       "138     21     of  1  0.090909  1.897120  0.172465\n",
       "139     21  those  1  0.090909  2.995732  0.272339\n",
       "\n",
       "[140 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tidytextpy import bind_tf_idf\n",
    "from plydata import count, group_by, ungroup\n",
    "\n",
    "tfidfs = (df\n",
    "          >> unnest_tokens(output='word', input='text')\n",
    "          >> count('docid', 'word')\n",
    "          >> bind_tf_idf(term='word', document='docid', n='n')\n",
    "         )\n",
    "\n",
    "tfidfs"
   ]
  }
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